Li Zeng

Research

Associate Professor Department of Statistics and Data Science

Dr Li is currently an associate professor in the Department of Statistics and Data Science, Southern University of Science and Technology. Previously she was a postdoctoral fellow in the Department of Statistics at the Pennsylvania State University. Dr. Li obtained her Ph.D. degree from the Department of Statistics and Actuarial Science at the University of Hong Kong. Dr. Li’s research covers random matrix theory and high dimensional statistics. She has published papers in top statistical journals.

Personal Profile

Professional Experience:

Jan 2021-present，Department of Statistics and Data Science, Southern University of Science and Technology, Associate Professor

Aug 2019-Dec 2020, Department of Statistics and Data Science, Southern University of Science and Technology, Assistant Professor

Oct 2017-Aug 2019, Department of Statistics, Pennsylvania State University Eberly Postdoc Fellow, mentor: Prof. Runze Li

Apr 2017-Aug 2017, Department of Statistics, University of Washington, Seattle,Research Assistant, mentor: Dr. Fang Han

Sep 2012- Mar 2017, Department of Statistics and Actuarial Science, HKU, Teaching Assistant

Educational Background:

Apr 2017, The Unviersity of Hong Kong (HKU), Ph.D., Department of Statistics and Actuarial Science Advisor: Prof. Jianfeng Yao

Sep 2012, Renmin University of China, Beijing (RUC) M.Sc., School of Statistics

Sep 2009, Beijing Normal University, Beijing (BNU), B.Sc., School of Mathematical Science

Awards and Honors:

Sep 2012- Mar 2017, Department of Statistics And Actuarial Science, HKU Excellent Teaching Assistant Award (5 times)

Academic Services:

Referee Service. Journal of the Royal Statistical Society: Series B, Journal of the American Statistical Association, The Annals of Statistics, Biometrika, IEEE Transactions on Signal Processing Journal of Multivariate Analysis, IISE Transactions

Opennings:

Postdoc/Research Associate Positions are available. Applicants with strong background in probability/High dimensional statistics/Random Matrix Theory are particularly welcomed

Personal Profile

Research

Random Matrix Theory

High dimensional Statistics

Time Series Analysis

Teaching

Statistical Calculation and Software（Fall, UG）

High Dimensional Statistics（Spring, PG）

Publications Read More

Zeng Li, Cheng Wang, Qinwen Wang (2022). On eigenvalues of a high-dimensional Kendall’s rank correlation matrix with dependence. Science China Mathematics, accepted.
Zhehan Kan, Shuoshuo Chen, Zeng Li, Zhihai He (2022). Self-supervised Reciprocal Learning and Optimization of Structural Groups for Human Pose Estimation, Proceedings of the 17th European Conference on Computer Vision (ECCV), 2022.
Zeng Li, Qinwen Wang, Runze Li (2021). CLT for linear spectral statistics of large dimensional Kendall rank correlation matrices, The Annals of Statistics, 49(3), 1569-1593.
Zeng Li, Qinwen Wang, Chuanlong Xie, (2021). Asymptotic Normality and Confidence Intervals for Prediction Risk of the Min-norm Least Squares Estimator, in International Conference on Machine Learning (ICML), May 2021.
Zeng Li, Fang Han, Jianfeng Yao (2020). Asymptotic joint distribution of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model, The Annals of Statistics, 48(6), 3138-3160.
Zeng Li, Jianfeng Yao, Clifford Lam, Qiwei Yao (2019). On testing for high-dimensional white noise, The Annals of Statistics, 47(6), 3382-3412.
Weiming Li, Zeng Li, Jianfeng Yao (2018). Joint CLT for linear spectral statistics of dependent large dimensional sample covariance matrices, Scandinavian Journal of Statistics, 45(3), 699-728.
Zeng Li, Qinwen Wang, Jianfeng Yao (2017). Identifying number of factors from singular values of lagged sample auto-covariance matrix, The Annals of Statistics, 45(1), 257-288.
Zeng Li, Jianfeng Yao (2016). Testing the sphericity of a covariance matrix when the dimension is much larger than the sample size, Electronic Journal of Statistics, 10(2), 2973-3010.
Zeng Li, Guangming Pan, Jianfeng Yao (2015). On singular value distribution of large-dimensional auto-covariance matrices, Journal of Multivariate Analysis, 137, 119-140.
Chao Yu, Yue Fang, Zeng Li, Bo Zhang, Xujie Zhao (2014). Nonparametric estimation of high frequency spot volatility for Brownian semimartingale with jumps, Journal of Time Series Analysis, 35(6), 572-591.

News More

统计与数据科学系本科生导师见面会成功举行
2019-09-09
SUSTech Launches New Dept of Statistics and Data Science
2019-08-21

People Read More

Join us

The Department of Statistics and Data Science was established in April 2019. As a newly established department, we currently have 8 faculty members including 1 Chair Professor, 2 Professors, 2 Associate Professors , 2 Tenure-track Assistant Professors and 1 Visiting Assistant Professor and anticipant to rapidly expand to a team of more than 20 members. All faculty members of the department have extensive overseas study or work experiences. One member is an invited speaker at the International Congress of Mathematicians, an IMS Medallion Lecturer and a winner of the prestigious State Natural Science Award (2nd class). The department has 2 research directions, Statistics and Data Science, which cover a broad array of research areas including Biostatistics, Financial Statistics, Experiment Design, Clinical Trials, High Dimensional Data, Time series, Probability Theory, Data Science and Big Data Technology.

At present, the department has an undergraduate program as well as two graduate programs(M.Phil and PhD). The department is in the process of developing a Major Program in Data Science and Big Data Technology. In 2019, the first batch of 38 undergraduate students in Statistics received the bachelor degree from SUSTech. 5 M.Phil students in Statistics also graduated in the same year. There are currently 9 M.Phil students, 7 PhD students in Statistics and 2 Postdoctoral Fellows. As the department grows in size and strength, the number of graduate students is expected to rise substantially from the current level and the areas of graduate studies will also expand to include Data Science and Big Data Technology.

This is the age of big data which offers exciting opportunities to researchers in Statistics and Data Science. Our department is expected to grow rapidly in the coming years. We invite outstanding scholars to join us in our journey to become a first-class department in Statistics and Data Science. We also welcome brilliant undergraduate and graduate students to apply for our programs. At the same time, we actively recruit excellent postdoctoral candidates.